Indexing Slicing and Iterating

Once you have a NumPy array, indexing lets you access individual elements, slicing lets you extract sub-sections, and iteration lets you loop through its contents - three essential skills for working with real data.

Indexing

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print(arr[0])    # 10
print(arr[-1])   # 50 (last element)

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix[1, 2])   # 6 (row 1, column 2)

Slicing

Slicing uses the start:stop:step syntax to extract a range of elements without needing a loop.

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])     # [20 30 40]
print(arr[:3])      # [10 20 30]
print(arr[::2])     # [10 30 50] - every second element

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[:2, 1:])   # rows 0-1, columns 1 onward

Iterating Over Arrays

import numpy as np

arr = np.array([1, 2, 3])
for x in arr:
    print(x)

matrix = np.array([[1, 2], [3, 4]])
for row in matrix:
    print(row)

for value in np.nditer(matrix):   # iterate over every individual element
    print(value)

Coming Up Next

Next, you'll learn about NumPy array attributes - properties that tell you an array's shape, size, and data type.